ADAPTIVE CAMOUFLAGE PATTERN GENERATION TO DIFFERENT ENVIRONMENTS VIA CONTENT-AWARE STYLE TRANSFER

Visual camouflage is an effective means of protecting valuable assets that are vulnerable to theft, espionage, or other forms of malicious activity. To overcome the limitation of standardized camouflage patterns in certain environments, we need an innovative approach that adapts the camouflage pattern to the specific surroundings of the asset to be concealed. In this paper, we present a novel camouflage image generation method whose results change by the circumstances around the asset to be concealed using the style transfer. In order to remove the influence of stuff and objects that are not advantageous for camouflage in the surrounding, we propose a novel mechanism that introduces the contents-aware information into the calculation of style representation. Experimental results in various situations, including the snowy natural scene, show that the proposed method provides excellent adaptive camouflage outcomes while effectively suppressing conspicuous elements in the surrounding.